khurdula

We've open-sourced all code, and test sets. You can find them here: https://interfaze.ai/blog/introducing-structured-output-benc...

To validate the choices and configurations, feel free to give it a reading. We also breakdown our methodology in the blog and in-depth within the paper.

We've added opus 4.6 and 4.7 to our leaderboard, they perform very closely with sonnet 4.6. Feel free to checkout our updated blog again :D

hey! we've evaluated gpt 5.5 as well along with other frontier models. gemini and gemma models outperform it across all three modalities.

Open source models like glm 4.7 still compete closely with table toppers.

We've updated our leaderboard having evaluated frontier models gemini 3.1 pro, opus 4.6 & 4.7, glm 5.1, deepseek v4, Kimi K2.6 as well.

We're updating our leaderboard with these model scores, should be out soon :D

We do love Qwen! It can be an easy choice when confused looking at this leaderboard.

Yep, we will be adding it soon as well.

Due to high demand, we're adding it soon!

General hallucinations benchmarks tend to be knowledge specific like GPQA or MMLU but none specifically measure structured output end-to-end which is one of the biggest use case for LLMs.

Many developer workflows use LLMs to produce structured artifacts due to it's flexibility of consuming unstructured inputs.

"don't use an LLM"

Partially agree, that's what we're building towards at interfaze.ai a hybrid between transformers (LLMs) and traditional CNN/DNN architecture to solve this problem of "deterministic" output. This give devs the flexibility of custom schema definitions and unstructured input while still getting high quality structured output like you would get from a CNN models like EasyOCR.

The industry is moving toward using LLMs for more and more deterministic tasks so this benchmarks allows us to now measure it.

We saw that structured decoding didn't make a difference in the quality of the output.

Check out the paper section "6.3 Structured Decoding Ablation"

Paper: https://arxiv.org/pdf/2604.25359

We ran the comparison and saw no difference, so to keep the bench consistent since some models don't support structured decoding we used greedy decoding on all models.

Check out the "The JSON-pass vs Value-Accuracy gap" section in the blog. That was an eye opener.

While most models were great at producing JSON schema, they were pretty bad at producing accurate values.

In the graph you'll is almost a 20%-30% drop between the JSON schema pass vs the value accuracy.

Yeah we selected models that are most commonly integrated in developer workflows and being used for structured output. Typically those models tend to be in the low -mid cost range and with no or low reasoning.

For the benchmark, was kept consistent across all models and typically opus and 3.1 pro would be overkill and expensive even with reasoning off.

Good point tho, will add this point in the blog too :)

Also the benchmark is open source, so anyone can run a model on it and create a PR too, the leaderboard is dynamic and will automatically add that in.

"we hope to open-source future versions of the model."

Love to see it. Cheers!

We define determinism as a model behaving predictably, while also producing useful supporting metadata, like confidence scores from specialized DNNs/CNNs, not just text tokens generated as "scores".

So for the same kind of task, you can expect the same kind of output every time, without randomly breaking structured output or having to constantly change generation hyperparams.

Bruh, if it were priced at like $2,499 it would make sense, but this is just too much.

Damn, just visiting this site makes me want to reinstall Minecraft haha.

What if I said, we outperform them? Check this out: https://jigsawstack.com/blog/openai-audio-stt-vs-jigsawstack...

Are we supposed to use AMD GPUs for this to work? Or Does it work on any GPU?